Price Blocker
Many price analysis are theoretical and of less practical use. With that in mind, we created the Price Blocker. The intention was to have an operational and simple tool at hand, that gives the user easy and understandable results.
You deal with that by creating a mirror database, constructed around industry standards and a solid master database. If the master database (e.g. Nielsen Scan Data for your category) does not include all the needed description fields, you simply create a look up table, or an external database. With that in mind, you are good to Click & Go for further analysis. The pictures below are taken from the Norwegian Beer Market, and only extractions are shown for simplification purpose.
Step 1: Do your selection
The Alpha & Omega in creating a modular / granular analysis tool, is to have a solid and a structured master database. If you have that, everything is doable. The essence in the output is to understand the whys behind the whys; hence it tallies well with an objective and holistic Cat Man approach, where you cover such things as assortment, pricing, planograms etc.
The Price Blocker contains 21 category description fields, and nine various approaches to the analysis, where value, volume and unit sold are the basic three. In addition to that there are three various price levels (amount, unit sold and shelf price) to be selected. In markets with a lot of multipacks and other pack options, that is needful thing to open up for.
All in all, the Price Blocker has 33 various selections for both your ‘Selection’ and for the respective ‘Benchmark’. This is picture only shows an extraction of total number of selections. The red and the green are simply ‘mirror’ / duplicate databases. You can also adjust the sales bottom-up (at each price range / block), adjusted for your distribution. That way you get an apple to apple comparison to your competing benchmark.
Step 2: Design your tables
In Step 2, you design your tables and number of price levels. We normally do up to 14 columns (time periods) and 30 layers of pricing. This can either be per month or by rolling quarters; or as the example below. You can opt to include a LIFT analysis and a Factor analysis; either based on actual sales or adjusted by distribution. We recommend making 2 by 3 tables, where you both can see absolute numbers, percentages and the deltas.
Step 3: Aggregated price differences
By setting the stage correct through the first two steps, you can now start getting the perspective over time by comparing your selected ‘item’ and your benchmark. This will at the very basic level tell you why you grow or not. This can be done over two or three time periods, and will then be your ‘alibi’ for how your sales / shares are trending. This is only at a simplistic and transactional level, and can be used as a starting point for further elastic analysis. You will now see how you compare to your desired benchmark.
Step 4: LIFT & Brand Strength Analysis
As much as you can’t look at pricing in isolation, you should not look into sales without understanding execution. If you have a ‘Store Tracker’ built on In-market-standards, you should link that to your sales data. When that is in place, you will have a far better platform for creating a right execution daily. However; if you do not have a ‘Store Tracker’, your sales data linked to master data (and category standards), will be a second best option to evaluate your LIFT option and your internal / relative brand strength, as shown in the graphs below.
This model can both be your sword for growth, or your shield for protection in case the retailer threatens to delist your product. Do not misunderstand us, as we are all in favor of removing SMCG (slow moving consumer goods), but if the retailer is making a mistake, this tool can be a shield for a follow-up discussion. Make sense? The relative brand strength will then be a validating verdict of the retailer’s challenge. If you score positive, the SKU should stay. In case it falls below; check for alternative routes. We will look more into that later under the unit of assortment optimization.
Step 5: Max-Min-Median
One thing is to see all the numbers in one go, an other thing is to simplify the overall elasticity & dynamics between your selected item (here Product A) and the desired benchmark (here: Product B). This simple graph, tells you quite a lot in one go. First of all it visualizes the span between the highest and the lowest price points for both products. It also plots the mean / average price points, and the differences between the maxes and the mins. Only by a simple glance, you can see that Product A is a stronger brand / product / supplier (all based on your selection in step 1). The next move will be to create elasticity analysis. It sounds perhaps difficult, but it is not. More about that later.
The elasticity
A simple overview over the span in pricing and 80% sales plot / span.
